Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction
[EN] Dendrograms are a way to represent relationships between organisms. Nowadays, these are inferred based on the comparison of genes or protein sequences by taking into account their differences and similarities. The genetic material of choice for the sequence alignments (all the genes or sets of...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/160086 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/160086 |
| Access Level: | acceso abierto |
| Palabra clave: | Dendrograms Statistical analysis MATEMATICA APLICADA FISICA APLICADA 17.- Fortalecer los medios de ejecución y reavivar la alianza mundial para el desarrollo sostenible 07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos |
| Sumario: | [EN] Dendrograms are a way to represent relationships between organisms. Nowadays, these are inferred based on the comparison of genes or protein sequences by taking into account their differences and similarities. The genetic material of choice for the sequence alignments (all the genes or sets of genes) results in distinct inferred dendrograms. In this work, we evaluate differences between dendrograms reconstructed with different methodologies and for different sets of organisms chosen at random from a much larger set. A statistical analysis is performed to estimate fluctuations between the results obtained from the different methodologies that allows us to validate a systematic approach, based on the comparison of the organisms' metabolic networks for inferring dendrograms. This has the advantage that it allows the comparison of organisms very far away in the evolutionary tree even if they have no known ortholog gene in common. Our results show that dendrograms built using information from metabolic networks are similar to the standard sequence-based dendrograms and can be a complement to them. |
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